Upload app.py

app.py

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+# import gradio as gr
+# from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+# from peft import PeftModel, PeftConfig
+
+# # Load tokenizer
+# tokenizer = AutoTokenizer.from_pretrained(".")
+
+# # Load base model with quantization
+# bnb_config = BitsAndBytesConfig(load_in_4bit=True)
+# base_model = AutoModelForCausalLM.from_pretrained(
+#     "unsloth/Meta-Llama-3.1-8B-bnb-4bit",  # same base you fine-tuned
+#     quantization_config=bnb_config,
+#     device_map="auto"
+# )
+
+# # Load LoRA adapters
+# model = PeftModel.from_pretrained(base_model, ".")
+
+# # Create Gradio Interface
+# def generate_response(prompt):
+#     inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+#     outputs = model.generate(**inputs, max_new_tokens=200, do_sample=True, temperature=0.7)
+#     return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# gr.Interface(
+#     fn=generate_response,
+#     inputs=gr.Textbox(label="Enter your instruction"),
+#     outputs=gr.Textbox(label="Model response"),
+#     title="LLaMA 3 - Fine-tuned Model"
+# ).launch()
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+import gradio as gr
+
+# Load base model from HF Hub
+base_model_name = "unsloth/Llama-3.2-1B"
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+
+# Load base model (set torch_dtype if needed)
+model = AutoModelForCausalLM.from_pretrained(base_model_name, torch_dtype=torch.float16)
+
+# Load LoRA adapters from local files in Space
+adapter_path = "./"  # If adapter files are in root or specify folder name
+model = PeftModel.from_pretrained(model, adapter_path)
+
+model.eval()
+
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt").to("cpu")  # Use "cuda" if GPU available
+    outputs = model.generate(**inputs, max_new_tokens=100)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+iface = gr.Interface(fn=predict, inputs="text", outputs="text", title="LoRA Model Demo")
+iface.launch()